HumanBase applies machine learning algorithms to learn biological assocations from massive genomic data collections. These integrative analyses reach beyond existing “biological knowledge” represented in the literature to identify novel, data-driven associations.
HumanBase models tissue-specific gene interactions by leveraging experimentally verified tissue expression and gene function to learn from an immense data compendium of diverse tissues and cell-types. The resulting functional networks accurately capture tissue-specific gene function.
Tissue-specific networks can guide genome-wide association (GWAS) analysis by effectively reprioritizing the associations from a GWAS study. With NetWAS (Network-guided GWAS Analysis), HumanBase can aide researchers in identifying additional disease-associated genes.